{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "68813a0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "有1个gpu可用\n",
      "gpu设备0:NVIDIA A800 80GB PCIe\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "if torch.cuda.is_available():\n",
    "    device_count = torch.cuda.device_count()\n",
    "    print(f\"有{device_count}个gpu可用\")\n",
    "    for i in range(device_count):\n",
    "        print(f\"gpu设备{i}:{torch.cuda.get_device_name(i)}\")\n",
    "else:\n",
    "    print('无gpu可用')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "685657c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "标签为：5\n"
     ]
    }
   ],
   "source": [
    "from torchvision import transforms,datasets\n",
    "from torch.utils.data import DataLoader\n",
    "batch_size = 60\n",
    "transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.1307),(0.3081)),\n",
    "    transforms.Resize((224,224))\n",
    "])\n",
    "\n",
    "train_dataset = datasets.MNIST(root='../dataset/mnist',train = True,\n",
    "                              download = True,\n",
    "                              transform=transform)\n",
    "test_dataset = datasets.MNIST(root='../dataset/mnist',train = False,\n",
    "                             download = True,\n",
    "                             transform=transform)\n",
    "train_loader = DataLoader(train_dataset,\n",
    "                          shuffle=True,\n",
    "                          batch_size=batch_size)\n",
    "test_loader = DataLoader(test_dataset,\n",
    "                        shuffle=True,\n",
    "                        batch_size=batch_size)\n",
    "import matplotlib.pyplot as plt\n",
    "image,label = train_dataset[1024]\n",
    "image = transforms.ToPILImage()(image)\n",
    "plt.imshow(image,cmap = 'gray')\n",
    "plt.show()\n",
    "print(f'标签为：{label}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "117bc4a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alexnet(\n",
       "  (features): Sequential(\n",
       "    (0): Conv2d(1, 96, kernel_size=(11, 11), stride=(4, 4))\n",
       "    (1): ReLU()\n",
       "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (3): Conv2d(96, 256, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
       "    (4): ReLU()\n",
       "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (6): Conv2d(256, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (7): ReLU()\n",
       "    (8): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (9): ReLU()\n",
       "    (10): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (11): ReLU()\n",
       "    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (fc): Sequential(\n",
       "    (0): Linear(in_features=6400, out_features=4096, bias=True)\n",
       "    (1): ReLU()\n",
       "    (2): Dropout(p=0.5, inplace=False)\n",
       "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
       "    (4): ReLU()\n",
       "    (5): Dropout(p=0.5, inplace=False)\n",
       "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
       "    (7): ReLU()\n",
       "    (8): Linear(in_features=1000, out_features=10, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch.nn.functional as F\n",
    "import torch.nn as nn\n",
    "\n",
    "class Alexnet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Alexnet, self).__init__()\n",
    "        self.features = nn.Sequential(\n",
    "            nn.Conv2d(1, 96, 11, 4, 0),\n",
    "            nn.ReLU(),  # 修正为 nn.ReLU()\n",
    "            nn.MaxPool2d(3, 2),\n",
    "            nn.Conv2d(96, 256, 5, 1, 2),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(3, 2),\n",
    "            nn.Conv2d(256, 384, 3, 1, 1),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(384, 384, 3, 1, 1),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(384, 256, 3, 1, 1),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(3, 2)\n",
    "        )\n",
    "        self.fc = nn.Sequential(  # 修正为 nn.Sequential\n",
    "            nn.Linear(256 * 5 * 5, 4096),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.5),\n",
    "            nn.Linear(4096, 4096),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.5),\n",
    "            nn.Linear(4096, 1000),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(1000, 10)\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        batch_size = x.size(0)\n",
    "        x = self.features(x)\n",
    "        x = self.fc(x.view(batch_size, -1))\n",
    "        return x\n",
    "\n",
    "model = Alexnet()\n",
    "device = torch.device('cuda:0')\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "839a1491",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5d93ed12",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(epoch):\n",
    "    running_loss = 0.0\n",
    "    for batch_idx, data in enumerate(train_loader,0):\n",
    "        inputs,labels=data\n",
    "        inputs,labels=inputs.to(device),labels.to(device)\n",
    "        optimizer.zero_grad()\n",
    "        outputs=model(inputs)\n",
    "        loss = criterion(outputs,labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        running_loss +=loss.item()\n",
    "        if batch_idx % 500 ==499:\n",
    "            print(f'{epoch+1,batch_idx+1} loss:{running_loss/batch_idx}')\n",
    "            \n",
    "def test():\n",
    "    correct = 0\n",
    "    total =0\n",
    "    with torch.no_grad():\n",
    "        for data in test_loader:\n",
    "            images,labels = data\n",
    "            images,labels = images.to(device),labels.to(device)\n",
    "            outputs = model(images)\n",
    "            _,predicted = torch.max(outputs.data,dim=1)\n",
    "            total += labels.size(0)\n",
    "            correct += (predicted ==labels).sum().item()\n",
    "    print(f'{100*correct/total}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "31b84d8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 500) loss:2.3027286209419877\n",
      "(1, 1000) loss:1.529025050404074\n",
      "(2, 500) loss:0.13315257684370588\n",
      "(2, 1000) loss:0.11356453961824198\n",
      "(3, 500) loss:0.0717135718176776\n",
      "(3, 1000) loss:0.06776002833460765\n",
      "(4, 500) loss:0.05004570467042026\n",
      "(4, 1000) loss:0.04841931213312936\n",
      "(5, 500) loss:0.04058166324624788\n",
      "(5, 1000) loss:0.039540884540406045\n",
      "(6, 500) loss:0.03676465713502541\n",
      "(6, 1000) loss:0.034258353370758055\n",
      "(7, 500) loss:0.025518065189218564\n",
      "(7, 1000) loss:0.02728961433146011\n",
      "(8, 500) loss:0.02117048370776015\n",
      "(8, 1000) loss:0.02429356339560179\n",
      "(9, 500) loss:0.021358630028942914\n",
      "(9, 1000) loss:0.021300242532046862\n",
      "(10, 500) loss:0.018624305602602025\n",
      "(10, 1000) loss:0.018997595047642483\n",
      "(11, 500) loss:0.01517810204344339\n",
      "(11, 1000) loss:0.016256593235736288\n",
      "(12, 500) loss:0.0139975603179666\n",
      "(12, 1000) loss:0.014741382400460497\n",
      "(13, 500) loss:0.01199084361591402\n",
      "(13, 1000) loss:0.013102945015371471\n",
      "(14, 500) loss:0.010750520272858282\n",
      "(14, 1000) loss:0.011021807737794749\n",
      "(15, 500) loss:0.008868663086607836\n",
      "(15, 1000) loss:0.009811831269666777\n",
      "(16, 500) loss:0.009750793173265345\n",
      "(16, 1000) loss:0.008847989880872285\n",
      "(17, 500) loss:0.007949026786365925\n",
      "(17, 1000) loss:0.008763140466685358\n",
      "(18, 500) loss:0.007424252180006002\n",
      "(18, 1000) loss:0.007724516939127191\n",
      "(19, 500) loss:0.005144088768394179\n",
      "(19, 1000) loss:0.006075138481943893\n",
      "(20, 500) loss:0.0059730708204818244\n",
      "(20, 1000) loss:0.0062386956080128265\n",
      "(21, 500) loss:0.005595071007153087\n",
      "(21, 1000) loss:0.005971355548257538\n",
      "(22, 500) loss:0.004460793494540403\n",
      "(22, 1000) loss:0.005538811093229126\n",
      "(23, 500) loss:0.005433919481204125\n",
      "(23, 1000) loss:0.005542995486614011\n",
      "(24, 500) loss:0.00361677786382885\n",
      "(24, 1000) loss:0.0044116148464266956\n",
      "(25, 500) loss:0.004548253261513955\n",
      "(25, 1000) loss:0.004819441060947286\n",
      "(26, 500) loss:0.003496952858372315\n",
      "(26, 1000) loss:0.0038082596926121755\n",
      "(27, 500) loss:0.004038475930991114\n",
      "(27, 1000) loss:0.003267492772045023\n",
      "(28, 500) loss:0.004579944304518922\n",
      "(28, 1000) loss:0.0048639473717803335\n",
      "(29, 500) loss:0.0035751848659755776\n",
      "(29, 1000) loss:0.0029684904474268138\n",
      "(30, 500) loss:0.0026224264381263895\n",
      "(30, 1000) loss:0.002451995039604851\n"
     ]
    }
   ],
   "source": [
    "for i in range(30):\n",
    "    train(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e4712b26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.31\n"
     ]
    }
   ],
   "source": [
    "test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1c06251",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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